A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)

Baokun He; Guihong Wan; Haim Schweitzer

doi:10.1609/aaai.v34i10.7175

A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)

Authors

Baokun He The University of Texas at Dallas
Guihong Wan The University of Texas at Dallas
Haim Schweitzer The University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaai.v34i10.7175

Abstract

Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a “bias trick” that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.

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Published

2020-04-03

How to Cite

He, B., Wan, G., & Schweitzer, H. (2020). A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13807-13808. https://doi.org/10.1609/aaai.v34i10.7175